Hoop House Gardening in the Wapekeka First Nation as an Extension of Land-Based Food Practices
Bibliographic record
Abstract
Rural Indigenous communities in Canada’s North face many challenges getting regular access to nutritious foods, primarily because of the high cost of market food, restricted availability of nutritious foods, and lack of government support for nutritious food programs. The consequences of food insecurity in this context are expressed in high rates of diabetes, heart disease, and childhood obesity. Many Indigenous communities are responding to issues related to healthy food access by attempting to rebuild local food capacity in their specific regions. Important first steps have been taken in developing local food initiatives, yet whether these initiatives are improving northern food security remains to be seen. We explore this question by working with the Oji-Cree First Nation in the community of Wapekeka, northern Ontario, to construct a hoop house and develop a school-based community gardening program. Using a community-based participatory approach, we determined that hoop house and gardening initiatives in rural, northern settings have the potential to build up local food production, develop the skills and knowledge of community members, engage youth in growing local food, and align with land-based food teachings. We show that despite widespread and multidimensional community hardships, there was considerable community buy-in and support for the project, which gives hope for future development and provides important insight for those seeking to initiate similar gardening, hoop house, or greenhouse initiatives in northern Indigenous communities.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".